Literature DB >> 35524912

U-Net-based image segmentation of the whole heart and four chambers on pediatric X-ray computed tomography.

Akifumi Yoshida1,2, Yohan Kondo3, Norihiko Yoshimura4, Tatsuya Kuramoto5, Akira Hasegawa6, Tsutomu Kanazawa5.   

Abstract

This study aimed to determine whether a U-Net-based segmentation method could be used to automatically extract regions of the whole heart and atrioventricular regions from pediatric cardiac computed tomography images with high accuracy. Pediatric cardiac contrast computed tomography images with no abnormalities (n = 20; patient age, 0-13 years; mean 5 years) were used for segmentation of the whole heart and each atrioventricular region using U-Net. Segmentation accuracy was evaluated using the Dice similarity coefficient. The mean Dice similarity coefficient for the whole-heart segmentation was high at 0.95. There were no significant differences between age categories. The median Dice similarity coefficients for segmentation of the atria and ventricles were good (> 0.86). There were significant differences between age categories at some sites. Differences in the Dice similarity coefficient may have occurred because the target diseases and examination procedures differed according to subject age. There was no clear tendency for similar values between subjects of school age, close to adulthood, and newborns; good agreement was obtained in all age categories. These results suggest that U-Net-based segmentation may be useful for automatic extraction of the whole heart and atrioventricular regions from pediatric computed tomography images.
© 2022. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.

Entities:  

Keywords:  Cardiac computed tomography; Deep learning; Heart; Pediatric; Segmentation

Mesh:

Year:  2022        PMID: 35524912     DOI: 10.1007/s12194-022-00657-3

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  2 in total

Review 1.  Computed Tomography Imaging in Patients with Congenital Heart Disease Part I: Rationale and Utility. An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT): Endorsed by the Society of Pediatric Radiology (SPR) and the North American Society of Cardiac Imaging (NASCI).

Authors:  B Kelly Han; Cynthia K Rigsby; Anthony Hlavacek; Jonathon Leipsic; Edward D Nicol; Marilyn J Siegel; Dianna Bardo; Suhny Abbara; Brian Ghoshhajra; John R Lesser; Subha Raman; Andrew M Crean
Journal:  J Cardiovasc Comput Tomogr       Date:  2015-07-23

2.  Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.

Authors:  Xiahai Zhuang; Lei Li; Christian Payer; Darko Štern; Martin Urschler; Mattias P Heinrich; Julien Oster; Chunliang Wang; Örjan Smedby; Cheng Bian; Xin Yang; Pheng-Ann Heng; Aliasghar Mortazi; Ulas Bagci; Guanyu Yang; Chenchen Sun; Gaetan Galisot; Jean-Yves Ramel; Thierry Brouard; Qianqian Tong; Weixin Si; Xiangyun Liao; Guodong Zeng; Zenglin Shi; Guoyan Zheng; Chengjia Wang; Tom MacGillivray; David Newby; Kawal Rhode; Sebastien Ourselin; Raad Mohiaddin; Jennifer Keegan; David Firmin; Guang Yang
Journal:  Med Image Anal       Date:  2019-08-01       Impact factor: 8.545

  2 in total

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